Authors:
Modhi Al Alshaikh
;
Gulden Uchyigit
and
Roger Evans
Affiliation:
University of Brighton, United Kingdom
Keyword(s):
Recommender Systems, Collaborative Filtering, Information Retrieval, Research Paper Recommendations.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Collaborative Filtering
;
Concept Mining
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Symbolic Systems
;
User Profiling and Recommender Systems
Abstract:
Our goal in this paper is to predict a user’s future interests in the research paper domain. Content-based recommender systems can recommend a set of papers that relate to a user’s current interests. However, they may not be able to predict a user’s future interests. Collaborative filtering approaches may predict a user’s future interests for movies, music or e-commerce domains. However, existing collaborative filtering approaches are not appropriate for the research paper domain, because they depend on large numbers of user ratings which are not available in the research paper domain. In this paper, we present a novel collaborative filtering method that does not depend on user ratings. Our novel method computes the similarity between users according to user profiles which are represented using the dynamic normalized tree of concepts model using the 2012 ACM Computing Classification System (CCS) ontology. Further, a community-centric tree of concepts is generated and used to make rec
ommendations. Offline evaluations are performed using the BibSonomy dataset. Our model is compared with two baselines. The results show that our model significantly outperforms the two baselines and avoids the problem of sparsity.
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